Evidence map›Paper›PMID 41787391›Full record

ArticleBMC neurology2026

Novel VSMC-associated biomarkers in intracranial aneurysm pathogenesis: a multi-omics and machine learning study.

Ruoyu Liu, Bin Xu, Zhenqiang Huang, Lujun Chen, Xiao Zheng, Tianwei Jiang

Abstract read
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Article in BMC neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ruoyu LiuDepartment of Neurosurgery, the Third Affiliated Hospital of Soochow University, Jiangsu Changzhou, 213003, China.
Bin XuDepartment of Tumor Biological Treatment, the Third Affiliated Hospital of Soochow University, Jiangsu Changzhou, 213003, China.
Zhenqiang HuangDepartment of Neurosurgery, the Third Affiliated Hospital of Soochow University, Jiangsu Changzhou, 213003, China.
Lujun ChenDepartment of Tumor Biological Treatment, the Third Affiliated Hospital of Soochow University, Jiangsu Changzhou, 213003, China.
Xiao ZhengDepartment of Tumor Biological Treatment, the Third Affiliated Hospital of Soochow University, Jiangsu Changzhou, 213003, China.
Tianwei JiangDepartment of Neurosurgery, the Third Affiliated Hospital of Soochow University, Jiangsu Changzhou, 213003, China. tianweij@suda.edu.cn.

Funding

Changzhou Clinical Medical Center CJ20180065Outstanding Talent of Changzhou "The 14th Five-Year Plan" High-Level Health Talents Training Project 2022CZBJ030
6 · The paper itself

Abstract

backgroundThis study aims to identify characteristic genes linked to vascular smooth muscle cells (VSMCs) and intracranial aneurysm (IA) formation and rupture, providing insights for early diagnosis, risk prediction, and treatment of IA.

methodsWe analyzed the GSE75436 and GSE13353 datasets from the GEO database, performing differential expression analysis with |log2FC|> 1 and adj p < 0.05. Gene Ontology (GO), KEGG, and Reactome pathway enrichment analyses were conducted. Using the GSE122897 dataset, Weighted Gene Co-expression Network Analysis (WGCNA) identified gene modules and hub genes associated with IA. Machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were applied to select differentially expressed hub genes. The expression patterns of marker genes were explored using the GSE193533 single-cell RNA sequencing dataset. Lastly, we assessed the predictive value of these genes in IA formation and rupture by plotting Receiver Operating Characteristic (ROC) curves and calculating the Area Under the Curve (AUC), thereby validating their sensitivity and specificity in clinical diagnostics.

resultsWe identified 1000 upregulated and 806 downregulated genes in IA compared to normal arteries, and 468 downregulated and 405 upregulated genes in ruptured IA tissue. WGCNA revealed key gene modules associated with IA. Machine learning identified genes such as COL5A2, CDH11, PLOD1, P3H4, PLIN2, and PLAUR. Single-cell analysis showed a phenotypic transition in VSMCs from contractile to synthetic, with these genes predominantly expressed in synthetic VSMCs. ROC analysis validated their excellent diagnostic performance for IA formation and rupture.

conclusionCOL5A2, CDH11, PLOD1, and P3H4 were identified as genes associated with IA formation, while PLIN2 and PLAUR were linked to IA rupture. These genes provide potential biomarkers for IA diagnosis and risk assessment.

Indexed as

Intracranial AneurysmMachine LearningMuscle, Smooth, VascularMyocytes, Smooth MuscleAneurysm, RupturedBiomarkersGene Expression ProfilingGene Regulatory NetworksHumansMultiomicsBiomarkersBioinformatics analysisBiomarkersIntracranial aneurysmMachine learningWeighted gene co-expression network analysis

Identifiers

PMID41787391
PMCPMC13072567

What Socratic holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.